M
Mugen Peng
Researcher at Beijing University of Posts and Telecommunications
Publications - 554
Citations - 16681
Mugen Peng is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Relay & Resource allocation. The author has an hindex of 51, co-authored 501 publications receiving 12800 citations. Previous affiliations of Mugen Peng include Peking University & Chinese Ministry of Education.
Papers
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Journal ArticleDOI
A Special Case of Multi-Way Relay Channel: When Beamforming is not Applicable
TL;DR: A new transmission protocol is proposed by aligning the messages from the same pair with the help of relay precoding, which can be avoided and intra-pair interference can be coped with by using network coding.
Posted Content
Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communications
TL;DR: A deep reinforcement learning (DRL)-based decentralized algorithm is proposed to maximize the sum capacity of vehicle-to-infrastructure users while meeting the latency and reliability requirements of V2V pairs, and the results show that the proposed DRL-based algorithm outperforms other decentralized baselines.
Posted Content
User Access Mode Selection in Fog Computing Based Radio Access Networks
Shi Yan,Mugen Peng,Wenbo Wang +2 more
TL;DR: In this article, the authors derived the coverage probability and ergodic rate for both F-AP users and device-to-device users by taking into account the different nodes locations, cache sizes as well as user access modes.
Proceedings ArticleDOI
A dynamic affinity propagation clustering algorithm for cell outage detection in self-healing networks
TL;DR: This work presents an automated cell outage detection mechanism in which a clustering technique called Dynamic Affinity Propagation (DAP) clustering algorithm is introduced and performance metrics are collected from the network during its regular operation and then fed into the algorithm to produce optimal clusters for further anomaly detection.
Journal ArticleDOI
A Realization of Fog-RAN Slicing via Deep Reinforcement Learning
Hongyu Xiang,Shi Yan,Mugen Peng +2 more
TL;DR: A deep reinforcement learning algorithm is proposed, whose core idea is that the cloud server makes proper decisions on the content caching and mode selection to maximize the reward performance under the dynamical channel state and cache status, which can be significantly improved by the proposal.